Watson Neil, Hendricks Sharief, Weaving Dan, Dalton-Barron Nicholas, Jones Ben, Stewart Theodor, Durbach Ian
University of Cape Town.
Leeds Beckett University.
Res Q Exerc Sport. 2025 Mar;96(1):34-52. doi: 10.1080/02701367.2024.2362253. Epub 2024 Jul 23.
Player movement in rugby league is complex, being spatiotemporal and multifaceted. Modeling this complexity to provide robust measures of player activity and load has proved difficult, with important aspects of player movement yet to be considered. These include the influence of time-varying covariates on player activity and the combination of different dimensions of player movement. Few studies have simultaneously categorized player activity into different activity states and investigated factors influencing the transition between states, or compared player activity and load profiles between matches and training. This study applied hidden Markov models (HMMs)-a data-driven, multivariate approach-to rugby league training and match GPS data to i) demonstrate how HMMs can combine multiple variables in a data-driven way to effectively categorize player movement states, ii) investigate the influence of two time-varying covariates, score difference and elapsed match time on player activity states, and iii) compare player activity and load profiles within and between training and match modalities. HMMs were fitted to player GPS, accelerometer and heart rate data of one English Super League team across 60 training sessions and 35 matches. Distinct activity states were detected for both matches and training, with transitions between states in matches influenced by score difference and elapsed time and clear differences in activity and load profiles between training and matches. HMMs can model the complexity of player movement to effectively profile player activity and load in rugby league and have the potential to facilitate new research across several sports.
橄榄球联盟中的球员运动十分复杂，具有时空性且涉及多个方面。事实证明，对这种复杂性进行建模以提供对球员活动和负荷的可靠衡量非常困难，因为球员运动的重要方面尚未得到考虑。这些方面包括时变协变量对球员活动的影响以及球员运动不同维度的组合。很少有研究同时将球员活动分类为不同的活动状态，并研究影响状态转换的因素，或者比较比赛和训练之间的球员活动和负荷情况。本研究将隐马尔可夫模型（HMMs）——一种数据驱动的多变量方法——应用于橄榄球联盟训练和比赛的GPS数据，以：i）展示HMMs如何以数据驱动的方式组合多个变量，从而有效地对球员运动状态进行分类；ii）研究两个时变协变量，即比分差距和比赛剩余时间对球员活动状态的影响；iii）比较训练和比赛模式内以及之间的球员活动和负荷情况。HMMs被应用于一支英国超级联赛球队在60次训练和35场比赛中的球员GPS、加速度计和心率数据。在比赛和训练中都检测到了不同的活动状态，比赛中状态之间的转换受比分差距和时间的影响，训练和比赛之间的活动和负荷情况存在明显差异。HMMs可以对球员运动的复杂性进行建模，以有效地描述橄榄球联盟中球员的活动和负荷情况，并且有潜力促进多个体育项目的新研究。
